The present invention relates to the field of human-resources management. More specifically, the invention relates a system and method for managing human resources with matching and mentoring services that operate on tag analytics, and implement skill gap analysis and remediation of the human resources based on the tag analytics.
Research in workforce management, or more broadly, human resources management, and learning focuses on creating innovative learning technologies to enhance worker performance, student performance, or more broadly, the performance of a member of an organized group of human resources. Such learning technologies represent and validate member (e.g., employee) skills, for example, for the purpose of facilitating matching people to opportunities, and supply chain modeling to optimize human resource planning and management.
Human resources management and learning may be described broadly to include at least the following: 1) hiring and firing; 2) salary and benefits administration; 3) creating innovative learning technologies to enhance student performance and increase learning through personalization and contextualization; 4) representing and validating employees' skills; 5) facilitating reskilling and matching people to available opportunities within an organization or workforce; and 6) applying supply chain modeling and mathematical algorithms to optimize human resource planning and management.
Several previously disclosed techniques in human resources management have addressed various aspects of employee skill identification, rating and ranking for purposes of improving performance via mentoring, classroom and self study and for optimizing matching of employees with new project requirements. For example, published US Patent Application No. 20060256953A1 to Pulaski, et al. (“the '953 application”), filed May 12, 2005, and incorporated by reference herein, discloses a method and system for improving workforce performance in a contact center. The '953 application describes technology for monitoring, in real time, the performance of call center agents to determine if their actions warrant timely performance support. The '953 application teaches the use of rules as the basis for the analysis of agent actions and also identify top performers so their best practices can be learned by underperformers.
Published US Patent Application No. 20050222899A1, to Varadarajan, et al. (“the '899 application”), filed Mar. 31, 2004, and incorporated by reference herein, discloses a system and method for skill management of knowledge workers in a Software Industry. The system and method track the initial skills of a member when joining an enterprise and track changes in skill sets as a result of additional training and project assignments. The skill assessment, updating and matching subsystems as taught by the '899 application rely on a common dictionary of software development processes known to practitioners in the software industry.
Published US Patent Application No. 20030182178A1, to D'Elena et al. (“the '178 application”), filed Mar. 21, 2002, and incorporated by reference herein, discloses a system and method for skill proficiencies acquisitions to identify skill gaps based on a comparison of an employee's skill profile and a job role skill profile and recommend various skill acquisition options suitable for an employee's work routine and learning style. Published US Patent Application No. 20020198765A1, to Magrino et al. (“the '765 application”), filed Mar. 20, 2002, and incorporated by reference herein, discloses a human capital management performance capability matching system and methods to rank the skill sets of members of a workforce by applying analytics to various artifacts associated with an employee including a free form text description of their skills. The '765 application describes an attempt to overcome some of the limitations of conventional human capital management systems by providing a more flexible way to recognize the unique skills of employees and to update categorization systems in newly developing skill areas.
The disclosed techniques have brought technical discipline and increased objectivity to human resources management processes. Viewed in aggregate, they dynamically update employee skill profiles and skill set definitions, make use of text mining to extract skill set metadata from documents, and attempt to match remedial resources with learning style. However, these techniques do not explicitly leverage text mining to identify concepts and their associated concepts. They do not include social networking data, including social networking tagging data, in their analytics. Furthermore, these techniques do not transparently leverage the activities associated with the data inputs used for skill assessment for the delivery of services focused on performance improvement.
Mentoring has proven to be an important ingredient in the growth of careers for individuals within a business or workforce enterprise. Mentoring within a workforce facilitates the transfer of skills to new employees as well as to current employees seeking to advance their careers. This benefits corporations by improving their ability to adapt to change. Tools to facilitate the initial matching of mentors and mentees, as well as to support the ongoing mentoring relationship have included sophisticated algorithms for matching profiles manually created by mentors and mentees. Known mentoring tools include the use of electronic collaborative environments including synchronous and asynchronous tools to enable one-to-one (“1:1”) and one-to-many (“1:many”) mentoring in cases where mentors and mentees are not co-located, or are always available at the same time.
That is, conventional “customized user experience” services and other tools for matching mentors and mentees require that an extensive profile be completed for each member that will be included as mentor or mentee rather than using existing information to facilitate the matching process. Once the match between mentor and mentee is established, known or commercially available tools support the ongoing delivery of the mentoring services. But conventional tools require manual repackaging or grouping of relevant content by the mentor before the same relevant content may be accessed by the mentee. The process of providing relevant content as part of ongoing mentoring services is for the mentor an extra task, which is not integrated into their day-to-day work.
For that matter, the field of social networking has shown explosive growth in the past few years. Social network analysis enables enterprises to identify members, e.g., employees, who are essential to internal communication processes, regardless of their position in the hierarchical organization chart. Social networking tools and application programs are known to identify employees not actively involved with communication processes, but possibly in need of mentoring to increase their skills so that they are more actively engaged by other employees. (“It's Who You Know”, IBM Think Research, July 2005, Kate Ehrlich, Inga Carboni) http://domino.research.ibm.com/comm/wwwr_thinkresearch.nsf/pages/20050706_think.shtml).
A variety of emerging web-based social networking services help people to find others of similar interests, and to share relevant content via new technologies such as blogs, wikis, and podcasts. For example, Really Simple Syndication™ (RSS) is a conventional tool or application program that enables individuals to keep up to date on content of interest without having to visit many different web sites. The content matching an individual's criteria is “pulled” to their RSS client via syndication services. RSS uses tagging, whereby individuals label content with meaningful terms and share the tags publicly, for improved information discovery. The tagging is useful where the content author or a subsequent viewer (if allowed) has tagged the content (of a file comprising an individual's data) with a label meaningful to the individual wishing to be informed of the content. RSS feeds based on criteria including specific tags have enabled individuals to focus on content of specific interest to them, both externally on the Internet and internally, via Intranets.
One example of the use of intranet based tagging to facilitate customized RSS feeds is the use of IBM's Dogear™ application. Dogear enables focusing on specific tagging by IBM subject matter experts. The tag feeds, with their associated URLs, are syndicated from Dogear to any RSS aggregator available to an IBM employee. As an example, an IBM employee “A” may focus on an IBM employee “B” social network analysis (“sna”) tags by creating a Dogear RSS feed. Employee A may thereafter view the feed of employee B's sna tags in employee A's RSS aggregator.
Focused RSS feeds of tags could be used for skill remediation via “1:1” or “1:many” mentoring or e-learning. The application is limited, however, due to the complexity in identifying where different tags have the same semantics, or where the same tag can have different semantics depending on the context. Recent research, Zhang, et al., Exploring Social Annotations for the Semantic Web”, Proc. of the 15th Intl. World Wide Web Conf., pp 417-426, ACM Press, 2006, suggests a “bottoms up” method to identify the concepts behind existing tags. The concepts are labeled with related tags that are applied to improve information discovery and search via filtering and ranking. This innovative “bottoms up” research does not rely on text analytics, and has not been applied to the task of facilitating matches between mentors who have mastered certain identified concepts and mentees who are lacking certain identified concepts, as revealed by an analysis of their tagging.
In Degeratu, et al., An Automatic Method for Constructing Domain-Specific Ontology Resources, Proc. of the Language Resources and Evaluation Conference (LREC), Lisbon, Portugal, 2004, the authors rely on text analytics to perform advanced domain-independent semantic analysis of unannotated collections of documents (a corpus of business registration documents is used for empirical evaluation). Degeratu use machine learning and statistical techniques to discover terms, link equivalent terms into concepts, and learn relationships between concepts (general similarity relationships, and also “named” semantic relationships). The techniques disclosed therein may be used to analyze the lexical choices of different employees, provide information on the different senses of tags based on the context in which the tags are used, and make suggestions for related content (documents, ideas, people). Such techniques, however, have not been applied to the task of using tag analytics to identify concepts and contexts associated with individual employees, or members of an organization, for the purpose of facilitating matches between mentors and mentees.
The innovative research on tag analytics has not extended to dynamically facilitate matching mentors with mentees in need of learning concepts in the same context that the same concepts have been mastered by the mentors (i.e., available mentors). More generally, and beyond the challenge of matching mentors and mentees using tagging, conventional applications and tools must address the difficulties associated with relying solely on the use of text analytics for determining correct labels for clusters. The known methods appear to lack robustness because they appear to have difficulty disambiguating between two senses of a tag. What would be welcomed to the skilled artisan is a service oriented architecture arranged to provide services to support organizations in the initial matching of mentors and mentees, and to support ongoing delivery of mentoring services based on tag analytics, and a method for providing such services and implementation.